Compressive sampling (CS) is based on the principle of sampling signals according to their sparsity or compressibility as opposed to the rate specified by the Nyquist/Shannon sampling theorem. Shannon's separation principle of source and channel coding cannot always be used to improve CS reconstruction performance in the presence of noise, including conditions when the channel is time-varying and there is a delay constraint, or when multiple channels are present as in broadcast/multicast applications. In this paper, we propose performing joint design of the source and channel coding to improve noisy CS performance in multicast and time varying channels.

One of the challenges in Cognitive Radio (CR) is efficiently monitoring a wideband radio frequency (RF) spectrum in order to identify unoccupied bands. Compressive Sensing (CS) has recently been proposed to address this problem. Typical CS techniques, however, involve random projections followed by a computationally intensive signal reconstruction process. Since spectral monitoring does require full signal reconstruction - only identification of occupied regions to avoid - we propose a novel spectrum monitoring approach based on the Nyquist Folding Receiver (NYFR) in conjunction with the Analytic Wavelet Transform. The NYFR performs analog compression via a non-uniform sampling process that induces a chirp-like modulation on each received signal. This induced modulation can be measured using time-frequency analysis techniques to determine the original RF band of origin without full signal reconstruction. This paper investigates the feasibility of using the Analytic Wavelet Transform to perform NYFR information recovery in support of CR wideband spectrum sensing.

This letter first reviews the multiuser bit-loading algorithms for discrete multitone systems. Then an improved algorithm is proposed, which allows several users to get additional bits in parallel. It reduces the number of iterations dramatically. A new fairness coefficient is also introduced to reduce the variance of the data rate among users